The present study proposes a Customer Behavior Mining Framework on the basis of data mining techniques in a telecom company. This framework takes into account the customers’ behavior patterns and predicts the way they may act in the future. Firstly, clustering technique is used to implement portfolio analysis and previous customers are divided based on socio-demographic features using k</em...

With the fast development of digital systems and concomitant information technologies, there is certainly an incipient spirit in the extensive overall economy to put together digital Customer Relationship Management (CRM) systems. This slanting is further more palpable in the telecommunications industry, in which businesses turn out to be increasingly digitalized. Customer churn prediction is a...

More and more literatures have researched the application of data mining technology in customer segmentation, and achieved sound effects. One of the key purposes of customer segmentation is customer retention. But the application of single data mining technology mentioned in previous literatures is unable to identify customer churn trend for adopting different actions on customer retention. Thi...

A huge number of customer data was collected by using of information technology in healthcare context. It is very important for healthcare providers to analyze this type of big data following the logic of customer relationship management. So here we propose an integration framework which can realize the customer segmentation and customer churn prediction together. To efficiently segment the cus...

In present days there is huge competition between various companies in the industry. Due to this companies pay more attention towards their customers rather than their product. They become aware of customer churn issue. Basically when a customer ceases one’s relationship with the company, this misfortune of relationship is known as customer churn. Various data mining approaches are used to pred...

Predicting customer churn is a classic data mining problem. Telecommunications providers have a long history of analyzing customer usage patterns to predict churn. Many other industries, such as banking, routinely analyze customer behavior to predict customer satisfaction and renewal rates. The Software as a Service (SaaS) model enables software vendors to collect customer usage data that is no...

Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting custome...

Customer is the heart and soul of any organization. The era of globalization and cut throat competition has changed the basic concept of marketing, now marketing is not confined to selling the products to the customers, but the objective is to reach to the hearts of the customers so that they feel belongingness towards the organizations and hence should remain the loyal customers. In the dynami...

Customer churn prediction is one of the most important problems in customer relationship management (CRM). Its aim is to retain valuable customers to maximize the profit of a company. To predict whether a customer will be a churner or non-churner, there are a number of data mining techniques applied for churn prediction, such as artificial neural networks, decision trees, and support vector mac...